Learning probabilities for causal networks

The author presents an unsupervised method to learn probabilities of random events. Learning is done by letting variables adaptively respond to positive and negative environmental stimuli. The basic learning rule is applied to learn prior and conditional probabilities for causal networks. By combining with a stochastic factor, this method is extended to learn probabilities of hidden causations, a type of event important in modeling causal relationships. In contrast to many existing neural network learning paradigms, probabilistic knowledge learned by this method is independent of any particular type of task. This method is especially suited for acquiring and updating knowledge in systems based on traditional artificial intelligence representation techniques.<>